# Finding Emotions in Faces: A Meta-Classifier

arXiv: https://arxiv.org/abs/2208.09678 · HTML: https://mlsanity.com/paper/2208.09678/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 20 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Face Alignment in Full Pose Range: A 3D Total Solution](https://mlsanity.com/paper/1804.01005/) (2018)
- [BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradient](https://mlsanity.com/paper/2004.08495/) (2020)

## Abstract (arXiv)

Machine learning has been used to recognize emotions in faces, typically by looking for 8 different emotional states (neutral, happy, sad, surprise, fear, disgust, anger and contempt). We consider two approaches: feature recognition based on facial landmarks and deep learning on all pixels; each produced 58% overall accuracy. However, they produced different results on different images and thus we propose a new meta-classifier combining these approaches. It produces far better results with 77% accuracy